49 research outputs found
Reducing Noise Level in Differential Privacy through Matrix Masking
Differential privacy schemes have been widely adopted in recent years to
address issues of data privacy protection. We propose a new Gaussian scheme
combining with another data protection technique, called random orthogonal
matrix masking, to achieve -differential privacy (DP)
more efficiently. We prove that the additional matrix masking significantly
reduces the rate of noise variance required in the Gaussian scheme to achieve
DP in big data setting. Specifically, when , , and the sample size exceeds the number of
attributes by , the required additive noise variance to
achieve -DP is reduced from
to . With much less noise
added, the resulting differential privacy protected pseudo data sets allow much
more accurate inferences, thus can significantly improve the scope of
application for differential privacy.Comment: 31 page